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An Intelligence Approach for Blood Pressure Estimation from Photoplethysmography Signal

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Health Information Science (HIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13705))

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Abstract

Commercial cuff-based Blood pressure (BP) devices are mainly not suitable or portable. To ease the measurement of BP devices, we proposed a new model for BP estimation based on photoplethysmography (PPG) signal. PPG signals are segmented into cycles using an improved peak detection algorithm. Then, each segment is mapped into a graph. Graph wavelets transform (GWT) is applied to each segment. The spectral graph features are extracted and tested to assess the BP. A ridge regression is employed to evaluated the BP with the reference of PPG. A publica dataset is used to evaluate the proposed model. The proposed model achieved good results and the obtained results are promising in improving the accuracy of BP estimation.

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Author information

Authors and Affiliations

  1. UniSQ College, University of Southern Queensland, Toowoomba, Australia

    Shahab Abdulla & Mohammed Diykh

  2. Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah, Iraq

    Mohammed Diykh, Sarmad K. D. AlKhafaji & Atheer Y. Oudah

  3. Information and Communication Technology Research Group, Scientific Research Centre, Al-Ayen University, Nasiriyah, Iraq

    Mohammed Diykh, Atheer Y. Oudah & Haydar Abdulameer Marhoon

  4. Department of Information Technology, College of Computer Science and Information Technology, University of Karbala, Karbala, Iraq

    Haydar Abdulameer Marhoon

  5. Directorate of Education, Iraqi Ministry of Education, Thi-Qar, Iraq

    Rand Ameen Azeez

Authors
  1. Shahab Abdulla
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  2. Mohammed Diykh
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  3. Sarmad K. D. AlKhafaji
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  4. Atheer Y. Oudah
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  5. Haydar Abdulameer Marhoon
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  6. Rand Ameen Azeez
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Corresponding author

Correspondence to Mohammed Diykh .

Editor information

Editors and Affiliations

  1. University of São Paulo, São Carlos, Brazil

    Agma Traina

  2. Victoria University, Melbourne, VIC, Australia

    Hua Wang

  3. Tsinghua University, Beijing, China

    Yong Zhang

  4. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  6. Swinburne University of Technology, Hawthorn, VIC, Australia

    Lu Chen

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Abdulla, S., Diykh, M., AlKhafaji, S.K.D., Oudah, A.Y., Marhoon, H.A., Azeez, R.A. (2022). An Intelligence Approach for Blood Pressure Estimation from Photoplethysmography Signal. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds) Health Information Science. HIS 2022. Lecture Notes in Computer Science, vol 13705. Springer, Cham. https://doi.org/10.1007/978-3-031-20627-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-20627-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20626-9

  • Online ISBN: 978-3-031-20627-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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